Our invention relates to the art of detecting ground vehicles by means of infrared sensors and imaging techniques. Perhaps the most important aspect of this art is to particularly identify specific vehicle types in a battlefield environment so that friend can be distinguished from foe. Ground vehicles generally are found in environments having visual clutter, which make the vehicle or other target of interest more difficult to detect. Clutter is defined verbally and mathematically in various ways. One way to define clutter is to equate it with all objects in the scene's background that look similar to the target or detract from the target. Clutter can also be defined as anything in the scene besides the target that competes for the attention of the viewer. One important facet of infrared imaging of vehicles is determining the probability that the vehicle will be detected over time, P(t), under given conditions such as atmospheric effects, distance, or characteristics of the viewing instrument. Only recently has it been suggested that clutter be an input variable in calculations of probability of detection. Our method for determining probability of detection uses a new definition of clutter and has a novel way of including clutter terms in P(t) determinations. Also, our method uses a new definition of the temperature differential between the target and background, so that our method is further distinguished from conventional methods.
Our method begins by acquiring and recording representations of -S a set of related infrared imaged scenes. The representations are pixelated and divided target blocks of pixels and background blocks of pixels. A plurality of .DELTA.T metrics is used on the blocked representations to derive a plurality of .DELTA.T values for each scenes. Then, by factor analysis, a relative .DELTA.T value for each scene is derived from the plurality of .DELTA.T values. The representations are also divided into cells of pixels in accordance with a plurality of clutter metrics whereupon these clutter metrics are used to derive a plurality of clutter values for each scene. Factor analysis is used to derive a relative clutter value for each scene. Then the relative .DELTA.T and clutter values are used to find the probability of detection of the targets of interest in the scenes.